Data Scientist Nanodegree

Convolutional Neural Networks

Project: Write an Algorithm for a Dog Identification App

This notebook walks you through one of the most popular Udacity projects across machine learning and artificial intellegence nanodegree programs. The goal is to classify images of dogs according to their breed.

If you are looking for a more guided capstone project related to deep learning and convolutional neural networks, this might be just it. Notice that even if you follow the notebook to creating your classifier, you must still create a blog post or deploy an application to fulfill the requirements of the capstone project.

Also notice, you may be able to use only parts of this notebook (for example certain coding portions or the data) without completing all parts and still meet all requirements of the capstone project.

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In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.

The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.

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Why We're Here

In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).

In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!

The Road Ahead

We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.

• Step 0: Import Datasets
• Step 1: Detect Humans
• Step 2: Detect Dogs
• Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
• Step 4: Use a CNN to Classify Dog Breeds (using Transfer Learning)
• Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)
• Step 6: Write your Algorithm
• Step 7: Test Your Algorithm

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Step 0: Import Datasets

Import Dog Dataset

In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the load_files function from the scikit-learn library:

• train_files, valid_files, test_files - numpy arrays containing file paths to images
• train_targets, valid_targets, test_targets - numpy arrays containing onehot-encoded classification labels
• dog_names - list of string-valued dog breed names for translating labels

from sklearn.datasets import load_files       
from keras.utils import np_utils
import numpy as np
from glob import glob

# define function to load train, test, and validation datasets
def load_dataset(path):
    data = load_files(path)
    dog_files = np.array(data['filenames'])
    dog_targets = np_utils.to_categorical(np.array(data['target']), 133)
    return dog_files, dog_targets

# load train, test, and validation datasets
train_files, train_targets = load_dataset('data/dog_images/train')
valid_files, valid_targets = load_dataset('data/dog_images/valid')
test_files, test_targets = load_dataset('data/dog_images/test')

# load list of dog names
dog_names = [item[20:-1] for item in sorted(glob("data/dog_images/train/*/"))]

# print statistics about the dataset
print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % len(np.hstack([train_files, valid_files, test_files])))
print('There are %d training dog images.' % len(train_files))
print('There are %d validation dog images.' % len(valid_files))
print('There are %d test dog images.'% len(test_files))

2022-12-11 23:24:49.207905: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-12-11 23:24:49.478043: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2022-12-11 23:24:49.478062: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2022-12-11 23:24:50.442352: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-12-11 23:24:50.442410: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-12-11 23:24:50.442420: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

There are 133 total dog categories.
There are 8351 total dog images.

There are 6680 training dog images.
There are 835 validation dog images.
There are 836 test dog images.

Import Human Dataset

In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array human_files.

import random
random.seed(8675309)

# load filenames in shuffled human dataset
human_files = np.array(glob("data/lfw/*/*"))
random.shuffle(human_files)

# print statistics about the dataset
print('There are %d total human images.' % len(human_files))

There are 13233 total human images.

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Step 1: Detect Humans

We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory.

In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.

import cv2                
import matplotlib.pyplot as plt                        
%matplotlib inline                               

# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')

# load color (BGR) image
img = cv2.imread(human_files[3])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# find faces in image
faces = face_cascade.detectMultiScale(gray)

# print number of faces detected in the image
print('Number of faces detected:', len(faces))

# get bounding box for each detected face
for (x,y,w,h) in faces:
    # add bounding box to color image
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()

Number of faces detected: 1

Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.

Write a Human Face Detector

We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.

# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) > 0

(IMPLEMENTATION) Assess the Human Face Detector

Question 1: Use the code cell below to test the performance of the face_detector function.

• What percentage of the first 100 images in human_files have a detected human face?
• What percentage of the first 100 images in dog_files have a detected human face?

Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.

Answer:

human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.

## TODO: Test the performance of the face_detector algorithm 
## on the images in human_files_short and dog_files_short.
human_detected_rs = [face_detector(human_face) for human_face in human_files_short] 
print(f"Human face detected per 100 human face files: {human_detected_rs.count(True)}%")
dog_detected_rs = [face_detector(dog_img) for dog_img in dog_files_short] 
print(f"Human face detected (mistake) per 100 dog image files {dog_detected_rs.count(True)}%")

Human face detected per 100 human face files: 100%
Human face detected (mistake) per 100 dog image files 12%

Question 2: This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?

Answer:

We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on each of the datasets.

## (Optional) TODO: Report the performance of another  
## face detection algorithm on the LFW dataset
### Feel free to use as many code cells as needed.

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Step 2: Detect Dogs

In this section, we use a pre-trained ResNet-50 model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories. Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.

# from keras.applications.resnet50 import ResNet50
from tensorflow.keras.applications.resnet50 import ResNet50
# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')

2022-12-11 23:25:04.508769: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/phu/project/dog-breeds-detector/dogbreedenv/lib/python3.10/site-packages/cv2/../../lib64:
2022-12-11 23:25:04.508789: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)
2022-12-11 23:25:04.508808: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (phu-ThinkPad-T14-Gen-1): /proc/driver/nvidia/version does not exist
2022-12-11 23:25:04.509390: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

Pre-process the Data

When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape

$$ (\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}), $$

where nb_samples corresponds to the total number of images (or samples), and rows, columns, and channels correspond to the number of rows, columns, and channels for each image, respectively.

The path_to_tensor function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape

$$ (1, 224, 224, 3). $$

The paths_to_tensor function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape

$$ (\text{nb_samples}, 224, 224, 3). $$

Here, nb_samples is the number of samples, or number of images, in the supplied array of image paths. It is best to think of nb_samples as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!

import keras.utils as image               
from tqdm import tqdm

def path_to_tensor(img_path):
    # loads RGB image as PIL.Image.Image type
    img = image.load_img(img_path, target_size=(224, 224))
    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
    x = image.img_to_array(img)
    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
    return np.expand_dims(x, axis=0)

def paths_to_tensor(img_paths):
    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
    return np.vstack(list_of_tensors)

Making Predictions with ResNet-50

Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function preprocess_input. If you're curious, you can check the code for preprocess_input here.

Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the predict method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the ResNet50_predict_labels function below.

By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this dictionary.

from tensorflow.keras.applications.resnet50 import decode_predictions
from tensorflow.keras.applications.imagenet_utils import preprocess_input
def ResNet50_predict_labels(img_path):
    # returns prediction vector for image located at img_path
    img = preprocess_input(path_to_tensor(img_path))
    return np.argmax(ResNet50_model.predict(img))

Write a Dog Detector

While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the ResNet50_predict_labels function above returns a value between 151 and 268 (inclusive).

We use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).

### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
    prediction = ResNet50_predict_labels(img_path)
    return ((prediction <= 268) & (prediction >= 151)) 

(IMPLEMENTATION) Assess the Dog Detector

Question 3: Use the code cell below to test the performance of your dog_detector function.

• What percentage of the images in human_files_short have a detected dog?
• What percentage of the images in dog_files_short have a detected dog?

Answer:

### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.
human_detected_rs = [dog_detector(human_face) for human_face in human_files_short] 
print(f"Dog detected (mistake) per 100 human face files: {human_detected_rs.count(True)}% ")
dog_detected_rs = [dog_detector(dog_img) for dog_img in dog_files_short] 
print(f"Dog detected per 100 dog image files {dog_detected_rs.count(True)}%")

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1/1 [==============================] - 0s 69ms/step
Dog detected per 100 dog image files 100%

---

Step 3: Create a CNN to Classify Dog Breeds (from Scratch)

Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.

Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.

We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.

Brittany | Welsh Springer Spaniel

• | -

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It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).

Curly-Coated Retriever | American Water Spaniel

• | -

|

Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.

Yellow Labrador | Chocolate Labrador | Black Labrador

• | -

| |

We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.

Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!

Pre-process the Data

We rescale the images by dividing every pixel in every image by 255.

from PIL import ImageFile                            
ImageFile.LOAD_TRUNCATED_IMAGES = True                 

# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
test_tensors = paths_to_tensor(test_files).astype('float32')/255

100%|██████████| 6680/6680 [00:18<00:00, 358.99it/s]
100%|██████████| 835/835 [00:02<00:00, 404.78it/s]
100%|██████████| 836/836 [00:02<00:00, 409.24it/s]

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:

    model.summary()

We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:

Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.

Answer: Layers of a CNN have multiple convolutional filters that scan all the feature matrix to reduce the dimension. This makes CNN a good choice for a huge number of parameters in image classification.

from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential
from keras.optimizers import Adam

model = Sequential()

### TODO: Define your architecture.
model.add(Conv2D(filters = 64, kernel_size = (3,3), activation ='relu', input_shape = (224,224,3)))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(filters = 32, kernel_size = (3,3), activation ='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(filters = 16, kernel_size = (3,3), activation ='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(filters = 8, kernel_size = (3,3), activation ='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(filters = 4, kernel_size = (3,3), activation ='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
# model.add(GlobalAveragePooling2D()
model.add(Dense(128, activation = "relu"))
model.add(Dense(64, activation = "relu"))
model.add(Dense(133, activation = "softmax"))

Compile the Model

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 222, 222, 64)      1792      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 111, 111, 64)     0         
 )                                                               
                                                                 
 conv2d_1 (Conv2D)           (None, 109, 109, 32)      18464     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 54, 54, 32)       0         
 2D)                                                             
                                                                 
 conv2d_2 (Conv2D)           (None, 52, 52, 16)        4624      
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 26, 26, 16)       0         
 2D)                                                             
                                                                 
 conv2d_3 (Conv2D)           (None, 24, 24, 8)         1160      
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 12, 12, 8)        0         
 2D)                                                             
                                                                 
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             (None, 222, 222, 64)      1792      
                                                                 
 max_pooling2d (MaxPooling2D  (None, 111, 111, 64)     0         
 )                                                               
                                                                 
 conv2d_1 (Conv2D)           (None, 109, 109, 32)      18464     
                                                                 
 max_pooling2d_1 (MaxPooling  (None, 54, 54, 32)       0         
 2D)                                                             
                                                                 
 conv2d_2 (Conv2D)           (None, 52, 52, 16)        4624      
                                                                 
 max_pooling2d_2 (MaxPooling  (None, 26, 26, 16)       0         
 2D)                                                             
                                                                 
 conv2d_3 (Conv2D)           (None, 24, 24, 8)         1160      
                                                                 
 max_pooling2d_3 (MaxPooling  (None, 12, 12, 8)        0         
 2D)                                                             
                                                                 
 conv2d_4 (Conv2D)           (None, 10, 10, 4)         292       
                                                                 
 max_pooling2d_4 (MaxPooling  (None, 5, 5, 4)          0         
 2D)                                                             
                                                                 
 flatten (Flatten)           (None, 100)               0         
                                                                 
 dense (Dense)               (None, 128)               12928     
                                                                 
 dense_1 (Dense)             (None, 64)                8256      
                                                                 
 dense_2 (Dense)             (None, 133)               8645      
                                                                 
=================================================================
Total params: 56,161
Trainable params: 56,161
Non-trainable params: 0
_________________________________________________________________

(IMPLEMENTATION) Train the Model

Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.

You are welcome to augment the training data, but this is not a requirement.

from keras.callbacks import ModelCheckpoint  

### TODO: specify the number of epochs that you would like to use to train the model.

epochs = 5

### Do NOT modify the code below this line.

checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', 
                               verbose=1, save_best_only=True)

model.fit(train_tensors, train_targets, 
          validation_data=(valid_tensors, valid_targets),
          epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)

Epoch 1/5

2022-12-11 23:25:55.286451: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 4022108160 exceeds 10% of free system memory.

334/334 [==============================] - ETA: 0s - loss: 4.8867 - accuracy: 0.0084
Epoch 1: val_loss improved from inf to 4.87417, saving model to saved_models/weights.best.from_scratch.hdf5
334/334 [==============================] - 213s 636ms/step - loss: 4.8867 - accuracy: 0.0084 - val_loss: 4.8742 - val_accuracy: 0.0108
Epoch 2/5
334/334 [==============================] - ETA: 0s - loss: 4.8607 - accuracy: 0.0124
Epoch 2: val_loss improved from 4.87417 to 4.79588, saving model to saved_models/weights.best.from_scratch.hdf5
334/334 [==============================] - 231s 691ms/step - loss: 4.8607 - accuracy: 0.0124 - val_loss: 4.7959 - val_accuracy: 0.0156
Epoch 3/5
334/334 [==============================] - ETA: 0s - loss: 4.7095 - accuracy: 0.0231
Epoch 3: val_loss improved from 4.79588 to 4.67161, saving model to saved_models/weights.best.from_scratch.hdf5
334/334 [==============================] - 232s 695ms/step - loss: 4.7095 - accuracy: 0.0231 - val_loss: 4.6716 - val_accuracy: 0.0228
Epoch 4/5
334/334 [==============================] - ETA: 0s - loss: 4.5459 - accuracy: 0.0329
Epoch 4: val_loss improved from 4.67161 to 4.55576, saving model to saved_models/weights.best.from_scratch.hdf5
334/334 [==============================] - 236s 708ms/step - loss: 4.5459 - accuracy: 0.0329 - val_loss: 4.5558 - val_accuracy: 0.0275
Epoch 5/5
334/334 [==============================] - ETA: 0s - loss: 4.4174 - accuracy: 0.0485
Epoch 5: val_loss improved from 4.55576 to 4.39901, saving model to saved_models/weights.best.from_scratch.hdf5
334/334 [==============================] - 229s 687ms/step - loss: 4.4174 - accuracy: 0.0485 - val_loss: 4.3990 - val_accuracy: 0.0527

<keras.callbacks.History at 0x7f0a105e1120>

Load the Model with the Best Validation Loss

model.load_weights('saved_models/weights.best.from_scratch.hdf5')

Test the Model

Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%.

# get index of predicted dog breed for each image in test set
dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]

# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)

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Test accuracy: 4.4258%

---

Step 4: Use a CNN to Classify Dog Breeds

To reduce training time without sacrificing accuracy, we show you how to train a CNN using transfer learning. In the following step, you will get a chance to use transfer learning to train your own CNN.

Obtain Bottleneck Features

bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')
train_VGG16 = bottleneck_features['train']
valid_VGG16 = bottleneck_features['valid']
test_VGG16 = bottleneck_features['test']

Model Architecture

The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.

VGG16_model = Sequential()
VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))
VGG16_model.add(Dense(133, activation='softmax'))

VGG16_model.summary()

Model: "sequential_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 global_average_pooling2d (G  (None, 512)              0         
 lobalAveragePooling2D)                                          
                                                                 
 dense_3 (Dense)             (None, 133)               68229     
                                                                 
=================================================================
Total params: 68,229
Trainable params: 68,229
Non-trainable params: 0
_________________________________________________________________
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 global_average_pooling2d (G  (None, 512)              0         
 lobalAveragePooling2D)                                          
                                                                 
 dense_3 (Dense)             (None, 133)               68229     
                                                                 
=================================================================
Total params: 68,229
Trainable params: 68,229
Non-trainable params: 0
_________________________________________________________________

Compile the Model

VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

Train the Model

checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5', 
                               verbose=1, save_best_only=True)

VGG16_model.fit(train_VGG16, train_targets, 
          validation_data=(valid_VGG16, valid_targets),
          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)

Epoch 1/20
321/334 [===========================>..] - ETA: 0s - loss: 7.8940 - accuracy: 0.2255
Epoch 1: val_loss improved from inf to 3.71487, saving model to saved_models/weights.best.VGG16.hdf5
334/334 [==============================] - 1s 4ms/step - loss: 7.7393 - accuracy: 0.2299 - val_loss: 3.7149 - val_accuracy: 0.4359
Epoch 2/20
331/334 [============================>.] - ETA: 0s - loss: 2.1300 - accuracy: 0.6044
Epoch 2: val_loss improved from 3.71487 to 2.80392, saving model to saved_models/weights.best.VGG16.hdf5
334/334 [==============================] - 1s 3ms/step - loss: 2.1296 - accuracy: 0.6043 - val_loss: 2.8039 - val_accuracy: 0.5497
Epoch 3/20
330/334 [============================>.] - ETA: 0s - loss: 1.2193 - accuracy: 0.7436
Epoch 3: val_loss improved from 2.80392 to 2.34359, saving model to saved_models/weights.best.VGG16.hdf5
334/334 [==============================] - 1s 3ms/step - loss: 1.2219 - accuracy: 0.7431 - val_loss: 2.3436 - val_accuracy: 0.6048
Epoch 4/20
322/334 [===========================>..] - ETA: 0s - loss: 0.7750 - accuracy: 0.8196
Epoch 4: val_loss improved from 2.34359 to 2.09485, saving model to saved_models/weights.best.VGG16.hdf5
334/334 [==============================] - 1s 3ms/step - loss: 0.7821 - accuracy: 0.8181 - val_loss: 2.0949 - val_accuracy: 0.6431
Epoch 5/20
323/334 [============================>.] - ETA: 0s - loss: 0.5459 - accuracy: 0.8647
Epoch 5: val_loss did not improve from 2.09485
334/334 [==============================] - 1s 3ms/step - loss: 0.5437 - accuracy: 0.8657 - val_loss: 2.0979 - val_accuracy: 0.6790
Epoch 6/20
319/334 [===========================>..] - ETA: 0s - loss: 0.3753 - accuracy: 0.9033
Epoch 6: val_loss did not improve from 2.09485
334/334 [==============================] - 1s 3ms/step - loss: 0.3746 - accuracy: 0.9033 - val_loss: 2.2218 - val_accuracy: 0.6527
Epoch 7/20
328/334 [============================>.] - ETA: 0s - loss: 0.2806 - accuracy: 0.9229
Epoch 7: val_loss did not improve from 2.09485
334/334 [==============================] - 1s 3ms/step - loss: 0.2820 - accuracy: 0.9225 - val_loss: 2.1644 - val_accuracy: 0.6850
Epoch 8/20
325/334 [============================>.] - ETA: 0s - loss: 0.2083 - accuracy: 0.9385
Epoch 8: val_loss did not improve from 2.09485
334/334 [==============================] - 1s 3ms/step - loss: 0.2082 - accuracy: 0.9388 - val_loss: 2.0986 - val_accuracy: 0.6970
Epoch 9/20
326/334 [============================>.] - ETA: 0s - loss: 0.1527 - accuracy: 0.9549
Epoch 9: val_loss improved from 2.09485 to 1.99185, saving model to saved_models/weights.best.VGG16.hdf5
334/334 [==============================] - 1s 3ms/step - loss: 0.1527 - accuracy: 0.9551 - val_loss: 1.9918 - val_accuracy: 0.7018
Epoch 10/20
325/334 [============================>.] - ETA: 0s - loss: 0.1178 - accuracy: 0.9643
Epoch 10: val_loss did not improve from 1.99185
334/334 [==============================] - 1s 3ms/step - loss: 0.1185 - accuracy: 0.9638 - val_loss: 2.1031 - val_accuracy: 0.6970
Epoch 11/20
329/334 [============================>.] - ETA: 0s - loss: 0.0903 - accuracy: 0.9737
Epoch 11: val_loss did not improve from 1.99185
334/334 [==============================] - 1s 3ms/step - loss: 0.0905 - accuracy: 0.9732 - val_loss: 2.1351 - val_accuracy: 0.6994
Epoch 12/20
324/334 [============================>.] - ETA: 0s - loss: 0.0659 - accuracy: 0.9784
Epoch 12: val_loss did not improve from 1.99185
334/334 [==============================] - 1s 3ms/step - loss: 0.0681 - accuracy: 0.9780 - val_loss: 2.1786 - val_accuracy: 0.7114
Epoch 13/20
319/334 [===========================>..] - ETA: 0s - loss: 0.0553 - accuracy: 0.9834
Epoch 13: val_loss improved from 1.99185 to 1.93515, saving model to saved_models/weights.best.VGG16.hdf5
334/334 [==============================] - 1s 3ms/step - loss: 0.0594 - accuracy: 0.9822 - val_loss: 1.9352 - val_accuracy: 0.7174
Epoch 14/20
328/334 [============================>.] - ETA: 0s - loss: 0.0442 - accuracy: 0.9864
Epoch 14: val_loss did not improve from 1.93515
334/334 [==============================] - 1s 3ms/step - loss: 0.0449 - accuracy: 0.9862 - val_loss: 1.9628 - val_accuracy: 0.7341
Epoch 15/20
331/334 [============================>.] - ETA: 0s - loss: 0.0339 - accuracy: 0.9885
Epoch 15: val_loss did not improve from 1.93515
334/334 [==============================] - 1s 3ms/step - loss: 0.0342 - accuracy: 0.9885 - val_loss: 2.1045 - val_accuracy: 0.7257
Epoch 16/20
334/334 [==============================] - ETA: 0s - loss: 0.0318 - accuracy: 0.9913
Epoch 16: val_loss did not improve from 1.93515
334/334 [==============================] - 1s 3ms/step - loss: 0.0318 - accuracy: 0.9913 - val_loss: 1.9402 - val_accuracy: 0.7377
Epoch 17/20
320/334 [===========================>..] - ETA: 0s - loss: 0.0235 - accuracy: 0.9933
Epoch 17: val_loss did not improve from 1.93515
334/334 [==============================] - 1s 3ms/step - loss: 0.0235 - accuracy: 0.9931 - val_loss: 2.0202 - val_accuracy: 0.7269
Epoch 18/20
327/334 [============================>.] - ETA: 0s - loss: 0.0187 - accuracy: 0.9960
Epoch 18: val_loss did not improve from 1.93515
334/334 [==============================] - 1s 3ms/step - loss: 0.0185 - accuracy: 0.9960 - val_loss: 2.0117 - val_accuracy: 0.7257
Epoch 19/20
317/334 [===========================>..] - ETA: 0s - loss: 0.0175 - accuracy: 0.9959
Epoch 19: val_loss did not improve from 1.93515
334/334 [==============================] - 1s 3ms/step - loss: 0.0177 - accuracy: 0.9958 - val_loss: 2.1336 - val_accuracy: 0.7281
Epoch 20/20
320/334 [===========================>..] - ETA: 0s - loss: 0.0138 - accuracy: 0.9962
Epoch 20: val_loss did not improve from 1.93515
334/334 [==============================] - 1s 3ms/step - loss: 0.0137 - accuracy: 0.9963 - val_loss: 1.9906 - val_accuracy: 0.7353

<keras.callbacks.History at 0x7f0a081d3ac0>

Load the Model with the Best Validation Loss

VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')

Test the Model

Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below.

# get index of predicted dog breed for each image in test set
VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]

# report test accuracy
test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)

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Test accuracy: 71.0526%

Predict Dog Breed with the Model

from extract_bottleneck_features import *

def VGG16_predict_breed(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_VGG16(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = VGG16_model.predict(bottleneck_feature)
    # return dog breed that is predicted by the model
    return dog_names[np.argmax(predicted_vector)]

---

Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)

You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.

In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, you must use the bottleneck features from a different pre-trained model. To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:

• VGG-19 bottleneck features
• ResNet-50 bottleneck features
• Inception bottleneck features
• Xception bottleneck features

The files are encoded as such:

Dog{network}Data.npz

where {network}, in the above filename, can be one of VGG19, Resnet50, InceptionV3, or Xception. Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the bottleneck_features/ folder in the repository.

(IMPLEMENTATION) Obtain Bottleneck Features

In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:

bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']

### TODO: Obtain bottleneck features from another pre-trained CNN.
bottleneck_features = np.load('bottleneck_features/DogResnet50Data.npz')
train_ResNet50 = bottleneck_features['train']
valid_ResNet50 = bottleneck_features['valid']
test_ResNet50 = bottleneck_features['test']

(IMPLEMENTATION) Model Architecture

Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:

    <your model's name>.summary()

Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.

Answer:

### TODO: Define your architecture.
ResNet_model = Sequential()
ResNet_model.add(GlobalAveragePooling2D(input_shape=train_ResNet50.shape[1:]))
ResNet_model.add(Dense(133, activation='softmax'))

ResNet_model.summary()

Model: "sequential_2"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 global_average_pooling2d_1   (None, 2048)             0         
 (GlobalAveragePooling2D)                                        
                                                                 
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 global_average_pooling2d_1   (None, 2048)             0         
 (GlobalAveragePooling2D)                                        
                                                                 
 dense_4 (Dense)             (None, 133)               272517    
                                                                 
=================================================================
Total params: 272,517
Trainable params: 272,517
Non-trainable params: 0
_________________________________________________________________

print(train_ResNet50.shape[1:])

(1, 1, 2048)

(IMPLEMENTATION) Compile the Model

### TODO: Compile the model.
ResNet_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

(IMPLEMENTATION) Train the Model

Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.

You are welcome to augment the training data, but this is not a requirement.

### TODO: Train the model.
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.ResNet.hdf5', 
                               verbose=1, save_best_only=True)

ResNet_model.fit(train_ResNet50, train_targets, 
          validation_data=(valid_ResNet50, valid_targets),
          epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)

Epoch 1/20
330/334 [============================>.] - ETA: 0s - loss: 1.6373 - accuracy: 0.6015
Epoch 1: val_loss improved from inf to 0.83975, saving model to saved_models/weights.best.ResNet.hdf5
334/334 [==============================] - 1s 3ms/step - loss: 1.6288 - accuracy: 0.6030 - val_loss: 0.8398 - val_accuracy: 0.7485
Epoch 2/20
325/334 [============================>.] - ETA: 0s - loss: 0.4399 - accuracy: 0.8631
Epoch 2: val_loss improved from 0.83975 to 0.74807, saving model to saved_models/weights.best.ResNet.hdf5
334/334 [==============================] - 1s 3ms/step - loss: 0.4381 - accuracy: 0.8636 - val_loss: 0.7481 - val_accuracy: 0.7772
Epoch 3/20
331/334 [============================>.] - ETA: 0s - loss: 0.2559 - accuracy: 0.9195
Epoch 3: val_loss improved from 0.74807 to 0.69896, saving model to saved_models/weights.best.ResNet.hdf5
334/334 [==============================] - 1s 3ms/step - loss: 0.2556 - accuracy: 0.9196 - val_loss: 0.6990 - val_accuracy: 0.7892
Epoch 4/20
326/334 [============================>.] - ETA: 0s - loss: 0.1715 - accuracy: 0.9452
Epoch 4: val_loss improved from 0.69896 to 0.63147, saving model to saved_models/weights.best.ResNet.hdf5
334/334 [==============================] - 1s 3ms/step - loss: 0.1724 - accuracy: 0.9454 - val_loss: 0.6315 - val_accuracy: 0.7916
Epoch 5/20
329/334 [============================>.] - ETA: 0s - loss: 0.1159 - accuracy: 0.9646
Epoch 5: val_loss did not improve from 0.63147
334/334 [==============================] - 1s 3ms/step - loss: 0.1169 - accuracy: 0.9641 - val_loss: 0.6329 - val_accuracy: 0.8048
Epoch 6/20
328/334 [============================>.] - ETA: 0s - loss: 0.0790 - accuracy: 0.9758
Epoch 6: val_loss improved from 0.63147 to 0.61492, saving model to saved_models/weights.best.ResNet.hdf5
334/334 [==============================] - 1s 3ms/step - loss: 0.0792 - accuracy: 0.9756 - val_loss: 0.6149 - val_accuracy: 0.8311
Epoch 7/20
332/334 [============================>.] - ETA: 0s - loss: 0.0555 - accuracy: 0.9863
Epoch 7: val_loss did not improve from 0.61492
334/334 [==============================] - 1s 3ms/step - loss: 0.0555 - accuracy: 0.9864 - val_loss: 0.6470 - val_accuracy: 0.8168
Epoch 8/20
325/334 [============================>.] - ETA: 0s - loss: 0.0408 - accuracy: 0.9900
Epoch 8: val_loss did not improve from 0.61492
334/334 [==============================] - 1s 3ms/step - loss: 0.0405 - accuracy: 0.9900 - val_loss: 0.6971 - val_accuracy: 0.8120
Epoch 9/20
328/334 [============================>.] - ETA: 0s - loss: 0.0273 - accuracy: 0.9950
Epoch 9: val_loss did not improve from 0.61492
334/334 [==============================] - 1s 3ms/step - loss: 0.0276 - accuracy: 0.9946 - val_loss: 0.6808 - val_accuracy: 0.8240
Epoch 10/20
328/334 [============================>.] - ETA: 0s - loss: 0.0209 - accuracy: 0.9953
Epoch 10: val_loss did not improve from 0.61492
334/334 [==============================] - 1s 3ms/step - loss: 0.0210 - accuracy: 0.9952 - val_loss: 0.6705 - val_accuracy: 0.8204
Epoch 11/20
322/334 [===========================>..] - ETA: 0s - loss: 0.0156 - accuracy: 0.9977
Epoch 11: val_loss did not improve from 0.61492
334/334 [==============================] - 1s 3ms/step - loss: 0.0158 - accuracy: 0.9976 - val_loss: 0.6673 - val_accuracy: 0.8216
Epoch 12/20
328/334 [============================>.] - ETA: 0s - loss: 0.0121 - accuracy: 0.9980
Epoch 12: val_loss did not improve from 0.61492
334/334 [==============================] - 1s 3ms/step - loss: 0.0120 - accuracy: 0.9981 - val_loss: 0.6345 - val_accuracy: 0.8287
Epoch 13/20
323/334 [============================>.] - ETA: 0s - loss: 0.0094 - accuracy: 0.9980
Epoch 13: val_loss did not improve from 0.61492
334/334 [==============================] - 1s 3ms/step - loss: 0.0092 - accuracy: 0.9981 - val_loss: 0.6585 - val_accuracy: 0.8359
Epoch 14/20
334/334 [==============================] - ETA: 0s - loss: 0.0088 - accuracy: 0.9979
Epoch 14: val_loss did not improve from 0.61492
334/334 [==============================] - 1s 3ms/step - loss: 0.0088 - accuracy: 0.9979 - val_loss: 0.6500 - val_accuracy: 0.8275
Epoch 15/20
319/334 [===========================>..] - ETA: 0s - loss: 0.0083 - accuracy: 0.9986
Epoch 15: val_loss did not improve from 0.61492
334/334 [==============================] - 1s 3ms/step - loss: 0.0080 - accuracy: 0.9987 - val_loss: 0.6319 - val_accuracy: 0.8347
Epoch 16/20
327/334 [============================>.] - ETA: 0s - loss: 0.0064 - accuracy: 0.9985
Epoch 16: val_loss did not improve from 0.61492
334/334 [==============================] - 1s 3ms/step - loss: 0.0064 - accuracy: 0.9985 - val_loss: 0.6453 - val_accuracy: 0.8359
Epoch 17/20
333/334 [============================>.] - ETA: 0s - loss: 0.0054 - accuracy: 0.9989
Epoch 17: val_loss did not improve from 0.61492
334/334 [==============================] - 1s 3ms/step - loss: 0.0054 - accuracy: 0.9990 - val_loss: 0.6446 - val_accuracy: 0.8383
Epoch 18/20
323/334 [============================>.] - ETA: 0s - loss: 0.0042 - accuracy: 0.9986
Epoch 18: val_loss did not improve from 0.61492
334/334 [==============================] - 1s 3ms/step - loss: 0.0041 - accuracy: 0.9987 - val_loss: 0.6668 - val_accuracy: 0.8431
Epoch 19/20
328/334 [============================>.] - ETA: 0s - loss: 0.0052 - accuracy: 0.9986
Epoch 19: val_loss did not improve from 0.61492
334/334 [==============================] - 1s 3ms/step - loss: 0.0051 - accuracy: 0.9987 - val_loss: 0.6594 - val_accuracy: 0.8359
Epoch 20/20
316/334 [===========================>..] - ETA: 0s - loss: 0.0043 - accuracy: 0.9987
Epoch 20: val_loss did not improve from 0.61492
334/334 [==============================] - 1s 3ms/step - loss: 0.0042 - accuracy: 0.9988 - val_loss: 0.6720 - val_accuracy: 0.8443

<keras.callbacks.History at 0x7f0a00137400>

(IMPLEMENTATION) Load the Model with the Best Validation Loss

### TODO: Load the model weights with the best validation loss.
ResNet_model.load_weights('saved_models/weights.best.ResNet.hdf5')

(IMPLEMENTATION) Test the Model

Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.

### TODO: Calculate classification accuracy on the test dataset.
ResNet_predictions = [np.argmax(ResNet_model.predict(np.expand_dims(feature, axis=0))) for feature in test_ResNet50]
test_accuracy = 100*np.sum(np.array(ResNet_predictions)==np.argmax(test_targets, axis=1))/len(ResNet_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)

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Test accuracy: 81.2201%

(IMPLEMENTATION) Predict Dog Breed with the Model

Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan_hound, etc) that is predicted by your model.

Similar to the analogous function in Step 5, your function should have three steps:

1. Extract the bottleneck features corresponding to the chosen CNN model.
2. Supply the bottleneck features as input to the model to return the predicted vector. Note that the argmax of this prediction vector gives the index of the predicted dog breed.
3. Use the dog_names array defined in Step 0 of this notebook to return the corresponding breed.

The functions to extract the bottleneck features can be found in extract_bottleneck_features.py, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function

extract_{network}

where {network}, in the above filename, should be one of VGG19, Resnet50, InceptionV3, or Xception.

### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.
# from extract_bottleneck_features import *
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
def extract_Resnet50(tensor):
    return ResNet50(weights='imagenet', include_top=False).predict(preprocess_input(tensor))

def resnet_predict_breed(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_Resnet50(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = ResNet_model.predict(bottleneck_feature)
    # return dog breed that is predicted by the model
    return dog_names[np.argmax(predicted_vector)]

---

Step 6: Write your Algorithm

Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,

• if a dog is detected in the image, return the predicted breed.
• if a human is detected in the image, return the resembling dog breed.
• if neither is detected in the image, provide output that indicates an error.

You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and dog_detector functions developed above. You are required to use your CNN from Step 5 to predict dog breed.

A sample image and output for our algorithm is provided below, but feel free to design your own user experience!

This photo looks like an Afghan Hound.

(IMPLEMENTATION) Write your Algorithm

### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.v
from PIL import Image
import os
import numpy as np


def dog_breed_detector(img_path):
    prediction = resnet_predict_breed(img_path)
    predict_name = os.path.basename(prediction).split(".")[1].replace("_"," ")
    predict_folder = np.array(glob("data/dog_images/train/" + os.path.basename(prediction) + "/*"))[0]
    predict_message = ''
    #Original
    base_name = f"Original: {os.path.basename(img_path)}"
    img_base = Image.open(img_path)
    #Predict
    not_dog_n_human = False
    if dog_detector(img_path):
        predict_message = f"Predict: {predict_name}"  
    elif face_detector(img_path) > 0:
        predict_message = f"Human: look like a {predict_name}"
    else:
        predict_message = "Predict: Neither dog or human!"
        not_dog_n_human = True
    #Predict plot
    try:
        predict_img = Image.open(predict_folder)
        if not_dog_n_human:         
            predict_img = img_base
    except Exception as e:
        print(e)
    return img_base, base_name, predict_img, predict_message

---

Step 7: Test Your Algorithm

In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?

(IMPLEMENTATION) Test Your Algorithm on Sample Images!

Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.

Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.

Answer: The prediction result is worse than expected. For dog breeds that share a similar heritage and body shape, there is a high probability of false predictions. We could try the following ways to improve the model:

• Increase the number of data images (for example, in the training set, there are only akita breeds but no shiba dogs - which we need to add)
• Increase the number of training episodes and try other feature detectors
• Because face detector detects a lot of false positives, we can build a neural network model to improve accuracy
• Apply Hyperparameter Tuning techniques

## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.
img_files = np.array(glob("web-app/imgs/*"))
fig = plt.figure(figsize=(9, len(img_files)*4))
columns = 2
rows = len(img_files)
ax = []
img_l, message_l =  [],[]
for img in img_files:
    img_base, base_name, predict_img, predict_message = dog_breed_detector(img)
    img_l.append(img_base)
    img_l.append(predict_img)
    message_l.append(base_name)
    message_l.append(predict_message)

for i in range(columns*rows):
    ax.append(fig.add_subplot(rows, columns, i+1) )
    ax[-1].set_title(message_l[i])
    plt.imshow(img_l[i],)
    

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WARNING:tensorflow:5 out of the last 843 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f0a0016a170> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
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WARNING:tensorflow:6 out of the last 846 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f0a08597e20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
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